code: def test_output_args(
      train_device, infer_device, small_classifier_and_preds
  ):
      with using_device_type(train_device):
          (
              model_path,
              model_type,
              X,
              xgb_preds,
          ) = small_classifier_and_preds
          fm = ForestInference.load(
              model_path, output_class=False, model_type=model_type
          )
      with using_device_type(infer_device):
          X = np.asarray(X)
          fil_preds = fm.predict(X)
          fil_preds = np.reshape(fil_preds, np.shape(xgb_preds))
  
      np.testing.assert_allclose(fil_preds, xgb_preds, atol=1e-3)
  
imports: import numpy
  import pytest
  import os
  import pandas
  from math import ceil
  from cuml.experimental import ForestInference
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml.internals.import_utils import has_lightgbm, has_xgboost
  from cuml.common.device_selection import using_device_type
  from sklearn.datasets import make_classification, make_regression
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.model_selection import train_test_split
  import treelite
label_desc: Use image node_id 10 for calling a global function with example usage: test_output_args(train_device, infer_device, small_classifier_and_preds) without return types